Exploding growth in mobile broadband usage has created a large number of new applications. Social networking is one of examples wherein many users rely on a mobile network to access the Internet. In such an application, it has been observed that an amount of downlink traffic from the Internet to users and an amount of uplink traffic from users to the Internet are relatively equal. This breaks traditional view on the Internet traffic that downlink load is typically heavier than uplink load, prompting mobile network operators to realize importance of optimizing network thereof for increasing amount of uplink traffic. Mobile network operators have begun to recognize the uplink as a key to potential new revenues, which comes, for examples, from applications such as cloud storage, Internet-of-Things (IoT), and intelligent traffic system (ITS). Therefore, for future of mobile broadband, optimizing uplink data communication performance is one of keys to effectively utilize limited radio resources and to maximize operational profit. One approach that has already been adopted in standards such as Third Generation Partnership Project (3GPP) Long-Term Evolution (LTE) described in non-patent literature (NPL) 1 is an employment of multiple antennas at both base station (BS) and user terminal (UT) to enable multiple-input multiple-output (MIMO) communications in both downlink and uplink, thereby improving efficiency of spectral usage in a radio network system.
Generally, in order for a base station with multiple-antennas to receive uplink data transmission from a user terminal, the base station performs following steps.
First, the base station estimates uplink channel by using a pilot signal transmitted from the user terminal. The pilot signal is also called Sounding Reference Signal (SRS) in LTE terminology. FIG. 7 illustrates a typical procedure for channel estimation described in NPL2. The typical channel estimation procedure simply multiplies a received signal received at each antenna with an inverse of the pilot signal.
Then, the base station schedules uplink data transmission of the user terminal based on the channel estimate.
Finally, the base station detects uplink data transmission from the user terminal.
Scheduling and detection of uplink data transmission have been well studied and regarding thereto, reference may be made to, for example, NPL3.
When a user terminal is power-limited and is under influence of deep-fading propagation environment, level of pilot signal transmitted from the user terminal and received at each antenna of a base station could be lower than level of noise. In that case, a typical channel estimation procedure in the base station would give poor channel estimation accuracy, resulting in degradation of uplink data reception performance such as high bit error rate and low throughput. An example of typical channel estimation procedures schematically illustrated in FIG. 7 will be later described.
A beamforming technique can be implemented in a base station prior to performing channel estimation in order to increase level of pilot signal received over level of noise. This can contribute to improvement of channel estimation accuracy and uplink data reception performance. Combination between beamforming and channel estimation is denoted herein as beamforming channel estimation procedure.
FIG. 8 schematically illustrates an example of typical beamforming channel estimation procedure. In a base station 10, there are provided, as a beamfomer, M multipliers 109, for each of N spatial directions, and N adders 110, where M is a predetermined integer not less than 2 and N is a predetermined positive integer. M multipliers 109 multiply signals respectively received at M antennas (multiple antennas) 101 with corresponding weighting coefficients to produce M weighted results. Each of adders 110 sums the M weighted results from M multipliers 109, thereby amplifying an energy of the pilot signal in a specified spatial direction θSi (i=, 1 . . . , N). Methods for determining weighting coefficients such as phased array method have been well studied and reference may be made to, for example, NPL4. The beamformed received signal can then be multiplied with an inverse of the pilot signal: 1/Xp(k) where k is a subcarrier index to obtain a channel estimate ˜Hsi(k) for a specified spatial direction θSi (i=, 1 . . . , N).
In practice, the beamforming channel estimation procedure usually selects a limited number of spatial directions to obtain channel estimates, because of computational complexity constraint. Also, a limited number of spatial directions θSi (i=, 1 . . . , N) are selected in order from the one that gives the largest beamformed received signal's energy to ensure the highest channel estimation accuracy. In other words, the beamforming channel estimation procedure introduces additional computational complexity to the typical channel estimation procedure in order to selectively improve channel estimates for the limited number of spatial directions. This is in contrast to the typical channel estimation procedure that gives unbiased channel estimates for all spatial directions at once.
[NPL 1]
    3GPP, “TS 36.213 v11.8.0: E-UTRA physical layer procedures (Release 11),” 3GPP, September 2014.[NPL 2]    J. C. Hancock and P. A. Wintz, “Signal detection theory,” McGraw-Hill, 1966.[NPL 3]    R. Zhang, “Scheduling for maximum capacity in SDMA/TDMA networks,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2002.[NPL 4]    N. Blaunstein and C. Christodoulou, “Radio propagation and adaptive antennas for wireless communication links,” John Wiley & Sons, 2007.